Many biological molecules are flexible in solution. This can be assessed by nuclear magnetic resonance (NMR) methods. Indeed, J scalar coupling constants and nuclear Overhauser effect (NOE) data are subject to different types of averaging when conformers are present in solution. In suc a case, it may not be possible to fit all observed NMR parameters with a single molecular conformation. We have developed a program, PDQPRO (Probability Distribution by Quadratic PROgramming), which determines the optimal probability distribution for a predefined pool of potential conformers by finding the best fit between calculated and experimental NMR parameters. Such an approach requires an independent sampling method producing a set of potential conformers. Analysis of a number of flexible test molecules with simulated NMR data has demonstrated that PDQPRO is able to recognize the correct conformers and calculate the correct probabilities, provided that those correct conformers are present in the pool of potential structures. Presently we are investigating the performance of PDQPRO in combination with various sampling methods. In particular, we have used the MDtar method developed by Torda et al. to generate an MD trajectory for a flexible RNA loop. The MDtar trajectory as a whole explained the existing NMR data; however, MD trajectories are not very suitable for a comprehensive structural analysis due to its size. The application of the PDQPRO to this trajectory reduced by an order of magnitude the number of conformers required to explain the experimental data. The Computer Graphics Laboratory resources are necessary for our project for graphical representation of structural ensembles, which is a non-trivial problem. In particular, we are using a number of MidasPlus delegates written for this special purpose by Eric Pettersen and David Konerding.